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    SELF-ORGANIZED RESIDUAL BLOCKS FOR IMAGE SUPER-RESOLUTION

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    Date
    2021
    Author
    Keleş, Onur
    Tekalp, A. Murat
    Malik, Junaid
    Kιranyaz, Serkan
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    Abstract
    It has become a standard practice to use the convolutional networks (ConvNet) with RELU non-linearity in image restoration and super-resolution (SR). Although the universal approximation theorem states that a multi-layer neural network can approximate any non-linear function with the desired precision, it does not reveal the best network architecture to do so. Recently, operational neural networks (ONNs) that choose the best non-linearity from a set of alternatives, and their "self-organized" variants (Self-ONN) that approximate any non-linearity via Taylor series have been proposed to address the well-known limitations and drawbacks of conventional ConvNets such as network homogeneity using only the McCulloch-Pitts neuron model. In this paper, we propose the concept of self-organized operational residual (SOR) blocks, and present hybrid network architectures combining regular residual and SOR blocks to strike a balance between the benefits of stronger non-linearity and the overall number of parameters. The experimental results demonstrate that the proposed architectures yield performance improvements in both PSNR and perceptual metrics.
    DOI/handle
    http://dx.doi.org/10.1109/ICIP42928.2021.9506260
    http://hdl.handle.net/10576/47892
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    • Electrical Engineering [‎2840‎ items ]

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